Dual-Band Power Divider with Wide Suppression Band: Artificial Intelligence Modeling for Performance Confirmation

被引:0
作者
Mohamadpour, Golshan [1 ]
Karimi, Salman [1 ]
Roshani, Saeed [2 ]
机构
[1] Lorestan Univ, Dept Elect Engn, Khorramabad, Iran
[2] Islamic Azad Univ, Dept Elect Engn, Kermanshah Branch, Kermanshah, Iran
来源
ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY | 2025年 / 13卷 / 01期
关键词
Dual band Wilkinson power divider; Harmonic suppression; Neural network; Resonator; HARMONIC SUPPRESSION; LOWPASS FILTER; DESIGN; FREQUENCY; COMPONENT; LINES;
D O I
10.14500/aro.11945
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
this paper, a planar dual-band Wilkinson power divider (DWPD) with a triangular-shaped resonator is designed. This work stands out from existing designs by addressing key limitations in conventional power dividers, i.e., physical size, harmonic suppression, and insertion loss. The proposed triangularshaped resonator has a compact size of 9.9 mm x 3.4 mm (0.26 lambda g x 0.09 lambda g), where lambda g is electrical wavelength at 5.9 GHz, and provides a wide suppression band from 7.1 GHz to 20.6 GHz with a 20 dB attenuation level. In the proposed DWPD structure, two triangularshaped resonators are used in two branches. It works at 3.6 GHz and 5.5 GHz with <0.1 dB insertion loss at both operating bands. The input and output return losses and ports isolation parameters at both bands are better than 20 dB, which show good performance of the divider at operating bands. Besides the acceptable performance, the proposed DWPD provides a wide suppression band from 6.8 GHz to 20.5 GHz with more than 20dB attenuation level. In the divider design, the neural network is employed to model a triangular-shaped resonator. The proposed neural network has two outputs (S11 and S21), and two hidden layers with eight neurons at each layer. The weights of each neuron are obtained using particle swarm optimization algorithms. The proposed neural network model has accurate results, and the mean relative error of the train and test data for both outputs is <0.1, which validates the accurate results of the proposed model.
引用
收藏
页码:27 / 33
页数:7
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